More recently, [1]} proposed Infidelity and Max-Sensitivity, two complementary metrics that measure the accuracy of a method and its robustness against small, insignificant perturbations, respectively. Recent work has shown that some feature attribution methods, much like neural networks themselves, are vulnerable to adversarial attacks [2]}. This makes the robustness of explanations an interesting property to measure in addition to the accuracy.
